gstsm: GSTSM

In gstsm: Generalized Spatial-Time Sequence Miner

GSTSM

Description

S3 class definition for GSTSM.

Usage

gstsm(sts_dataset, spatial_positions, gamma, beta, sigma)

Arguments

sts_dataset	STS dataset
spatial_positions	set of spatial positions
gamma	minimum temporal frequency
beta	minimum group size
sigma	maximum distance between group points

Details

This algorithm is designed to the identification of frequent sequences in STS datasets from the concept of Solid Ranged Groups (SRG). GSTSM is based on the candidate-generating principle. The goal is to start finding SRGs for sequences of size one. Then it explores the support and the number of occurrences of SRGs for larger sequences with a limited number of scans over the database.

Value

a GSTSM object

Examples

library("gstsm")

D <- as.data.frame(matrix(c("B", "B", "A", "C", "A",
              "C", "B", "C", "A", "B",
              "C", "C", "A", "C", "A",
              "B", "B", "D", "A", "B",
              "B", "D", "D", "B", "D"
            ), nrow = 5, ncol = 5, byrow = TRUE))

ponto <- c("p1", "p2", "p3", "p4", "p5")
x <- c(1, 2, 3, 4, 5)
y <- c(0, 0, 0, 0, 0)
z <- y
P <- data.frame(ponto=ponto, x=x, y=y, z=z, stringsAsFactors = FALSE)

gamma <- 0.8
beta <- 2
sigma <- 1

gstsm_object <- gstsm(D, P, gamma, beta, sigma)

result <- mine(gstsm_object)

gstsm documentation built on Oct. 20, 2022, 1:07 a.m.